Data Scientist

At Tessellate Imaging, Vishnu enjoy's working with and helping businesses understand the problem at hand and fully utilize the potential of their data. He mainly focus on analyzing data, automating processes, finding patterns, making predictions and quantifying the state of things with indicators. These in turn help the clients to streamline decision making, reduce costs, and improve user experiences in their endeavors to use Data Science and Machine Learning. Vishnu's main asset is the creative thinking that he bring to problem solving. His fields of interest include business intelligence, statistical computations, machine learning and data visualization.

INDUSTRIES SERVED

PAST WORK

Automated Data Visualization Generation

Machine Learning Engineer

Details:
- Designing data visualization as a machine learning problem to automate the visualization authoring process.
- Developed a model to author initial data visualizations from the input data specifications.
- These initial visualizations provide users with a better starting point in the data exploration process.
Responsibilities:
- Developing a pipeline and training a model to generate a variety of data visualizations with just the input data as the starting point.

Insight Generation - Call Center Industry

Data Scientist

Details:
- The modules helped in generating insights for a dashboard about the agent performance.
- Insights like the correlation between different performance metrics, trend, and seasonality of these metrics, identifying anomalous performance, etc. could be generated.
- Also, these insights could be generated at a different organizational level of the call center.
Responsibilities:
- Designed and developed different modules to generate a variety of insights from the agent performance input data.

House Price Prediction

Data Scientist

Details:
- Studied the correlation and covariance of different qualitative and quantitative features of a property, with its value.
- Experimented with different regression models to predict the property value.
Responsibilities:
- Using the qualitative and quantitative features of a house, trained a model to predict the price point of the property for tax evaluation purposes.

Product Recommendation

Machine Learning Engineer

Details:
- Collaborative or social filtering model helps us in learning the similarities in the tastes and preferences of different users and using the same to recommend relevant items.
- Using this model, both item-based filtering and user-based filtering can be achieved to recommend relevant products.
Responsibilities:
- Developed and trained models that would help recommend items of interests to the end user, using user feedback (explicit), user metadata and product metadata.

Resource Allocation Optimization

Data Scientist

Details:
- Helped the client, fine-tune their resource allocation strategy by finding the balance between the reach and frequency targets across the various customer segments.
- As a part of this team, I also played a pivotal role in helping the team operationalize the project, and design and develop diagnostics tools, to quickly summarize various sales & marketing metrics.
Responsibilities:
- Led a team to optimize the sales force effort allocation in order to maximize the output of sales pitches to the client’s valuable targets.

Sales-force Sizing

Data Scientist

Details:
- Based on the estimated effort required by the sales-force to market to the potential targets, the sales force size is adjusted.
Responsibilities:
- In my capacity as client expert, helped the team to process the sales rep activity data and customer sales data for estimating the optimal sales-force size.

Customer Segmentation for Marketing

Data Scientist

Details:
- Using the historical client and market sales data, the targets of a pharmaceutical company were segmented to identify the high-low valuable customers for marketing purposes.
Responsibilities:
- In my capacity as client data expert, helped the team to process the sales data for customer segmentation.

Improve vehicle ride quality by controlling the MR damper

Machine Learning Engineer

Details:
- Developed GP and ANN models using the system parameters of a quarter car model. The model was trained to autonomously control the feedback mechanism to the magnetorheological (MR) dampers in order to improve the ride quality (suspension) in a vehicle.
Responsibilities:
- Helped the team build the quarter car model, record system sensor data for different tests and train the GP and ANN models using the collected system sensor data.

Bearing Failure Prediction

Machine Learning Engineer

Details:
- Developed predictive models to foresee the timing, location, and severity of bearing failure in drilling and milling machines. The vibration signatures and acoustic data collected with the help of accelerometers and noise level meters respectively were used as indicators in the model.
Responsibilities:
- Led a team that designed and built a testing rig to collect the vibration signatures and acoustics data from the bearing component along with developing the numerical predictive models.